import math
import pdb
from functools import lru_cache, reduce
from operator import mul

import apex
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from einops import rearrange
from timm.models.layers import DropPath, trunc_normal_

from monai.networks.blocks import UnetBasicBlock


class Mlp(nn.Module):
    """Multilayer perceptron."""

    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


def window_partition(x, window_size):
    """
    Args:
        x: (B, D, H, W, C)
        window_size (tuple[int]): window size

    Returns:
        windows: (B*num_windows, window_size*window_size, C)
    """
    B, D, H, W, C = x.shape
    x = x.view(
        B,
        D // window_size[0],
        window_size[0],
        H // window_size[1],
        window_size[1],
        W // window_size[2],
        window_size[2],
        C,
    )
    windows = x.permute(0, 1, 3, 5, 2, 4, 6, 7).contiguous().view(-1, reduce(mul, window_size), C)
    return windows


def window_reverse(windows, window_size, B, D, H, W):
    """
    Args:
        windows: (B*num_windows, window_size, window_size, C)
        window_size (tuple[int]): Window size
        H (int): Height of image
        W (int): Width of image

    Returns:
        x: (B, D, H, W, C)
    """
    x = windows.view(
        B,
        D // window_size[0],
        H // window_size[1],
        W // window_size[2],
        window_size[0],
        window_size[1],
        window_size[2],
        -1,
    )
    x = x.permute(0, 1, 4, 2, 5, 3, 6, 7).contiguous().view(B, D, H, W, -1)
    return x


def get_window_size(x_size, window_size, shift_size=None):
    use_window_size = list(window_size)
    if shift_size is not None:
        use_shift_size = list(shift_size)
    for i in range(len(x_size)):
        if x_size[i] <= window_size[i]:
            use_window_size[i] = x_size[i]
            if shift_size is not None:
                use_shift_size[i] = 0

    if shift_size is None:
        return tuple(use_window_size)
    else:
        return tuple(use_window_size), tuple(use_shift_size)


class WindowAttention3D(nn.Module):
    """Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.
    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The temporal length, height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(self, dim, window_size, num_heads, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0):
        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wd, Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-0.5

        # define a parameter table of relative position bias
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1) * (2 * window_size[2] - 1), num_heads)
        )  # 2*Wd-1 * 2*Wh-1 * 2*Ww-1, nH

        # get pair-wise relative position index for each token inside the window
        coords_d = torch.arange(self.window_size[0])
        coords_h = torch.arange(self.window_size[1])
        coords_w = torch.arange(self.window_size[2])
        coords = torch.stack(torch.meshgrid(coords_d, coords_h, coords_w))  # 3, Wd, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 3, Wd*Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 3, Wd*Wh*Ww, Wd*Wh*Ww
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wd*Wh*Ww, Wd*Wh*Ww, 3
        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 2] += self.window_size[2] - 1

        relative_coords[:, :, 0] *= (2 * self.window_size[1] - 1) * (2 * self.window_size[2] - 1)
        relative_coords[:, :, 1] *= 2 * self.window_size[2] - 1
        relative_position_index = relative_coords.sum(-1)  # Wd*Wh*Ww, Wd*Wh*Ww
        self.register_buffer("relative_position_index", relative_position_index)

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        trunc_normal_(self.relative_position_bias_table, std=0.02)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, mask=None):
        """Forward function.
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, N, N) or None
        """
        B_, N, C = x.shape
        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]  # B_, nH, N, C

        q = q * self.scale
        attn = q @ k.transpose(-2, -1)

        index_clone = self.relative_position_index.clone()
        relative_position_bias = self.relative_position_bias_table[index_clone[:N, :N].reshape(-1)].reshape(
            N, N, -1
        )  # Wd*Wh*Ww,Wd*Wh*Ww,nH

        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wd*Wh*Ww, Wd*Wh*Ww
        attn = attn + relative_position_bias.unsqueeze(0)  # B_, nH, N, N

        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)
            attn = self.softmax(attn)
        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        x = (attn.float() @ v.float()).transpose(1, 2).reshape(B_, N, C)
        # x = self.proj(x.half())
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class SwinTransformerBlock3D(nn.Module):
    """Swin Transformer Block.

    Args:
        dim (int): Number of input channels.
        num_heads (int): Number of attention heads.
        window_size (tuple[int]): Window size.
        shift_size (tuple[int]): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(
        self,
        dim,
        num_heads,
        window_size=(2, 7, 7),
        shift_size=(0, 0, 0),
        mlp_ratio=4.0,
        qkv_bias=True,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        use_checkpoint=False,
    ):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        self.use_checkpoint = use_checkpoint

        assert 0 <= self.shift_size[0] < self.window_size[0], "shift_size must in 0-window_size"
        assert 0 <= self.shift_size[1] < self.window_size[1], "shift_size must in 0-window_size"
        assert 0 <= self.shift_size[2] < self.window_size[2], "shift_size must in 0-window_size"

        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention3D(
            dim,
            window_size=self.window_size,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop,
        )

        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

    def forward_part1(self, x, mask_matrix):
        B, D, H, W, C = x.shape
        window_size, shift_size = get_window_size((D, H, W), self.window_size, self.shift_size)
        x = self.norm1(x)
        pad_l = pad_t = pad_d0 = 0
        pad_d1 = (window_size[0] - D % window_size[0]) % window_size[0]
        pad_b = (window_size[1] - H % window_size[1]) % window_size[1]
        pad_r = (window_size[2] - W % window_size[2]) % window_size[2]
        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b, pad_d0, pad_d1))
        _, Dp, Hp, Wp, _ = x.shape
        # cyclic shift
        if any(i > 0 for i in shift_size):
            shifted_x = torch.roll(x, shifts=(-shift_size[0], -shift_size[1], -shift_size[2]), dims=(1, 2, 3))
            attn_mask = mask_matrix
        else:
            shifted_x = x
            attn_mask = None
        # partition windows
        x_windows = window_partition(shifted_x, window_size)  # B*nW, Wd*Wh*Ww, C
        # W-MSA/SW-MSA
        attn_windows = self.attn(x_windows, mask=attn_mask)  # B*nW, Wd*Wh*Ww, C
        # merge windows
        attn_windows = attn_windows.view(-1, *(window_size + (C,)))
        shifted_x = window_reverse(attn_windows, window_size, B, Dp, Hp, Wp)  # B D' H' W' C
        # reverse cyclic shift
        if any(i > 0 for i in shift_size):
            x = torch.roll(shifted_x, shifts=(shift_size[0], shift_size[1], shift_size[2]), dims=(1, 2, 3))
        else:
            x = shifted_x

        if pad_d1 > 0 or pad_r > 0 or pad_b > 0:
            x = x[:, :D, :H, :W, :].contiguous()
        return x

    def forward_part2(self, x):
        return self.drop_path(self.mlp(self.norm2(x)))

    def forward(self, x, mask_matrix):
        """Forward function.

        Args:
            x: Input feature, tensor size (B, D, H, W, C).
            mask_matrix: Attention mask for cyclic shift.
        """

        shortcut = x
        if self.use_checkpoint:
            x = checkpoint.checkpoint(self.forward_part1, x, mask_matrix)
        else:
            x = self.forward_part1(x, mask_matrix)
        x = shortcut + self.drop_path(x)

        if self.use_checkpoint:
            x = x + checkpoint.checkpoint(self.forward_part2, x)
        else:
            x = x + self.forward_part2(x)

        return x


class PatchMerging(nn.Module):
    """Patch Merging Layer

    Args:
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.reduction = nn.Linear(8 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(8 * dim)

    def forward(self, x):
        """Forward function.

        Args:
            x: Input feature, tensor size (B, D, H, W, C).
        """
        B, D, H, W, C = x.shape

        # padding
        pad_input = (H % 2 == 1) or (W % 2 == 1)
        if pad_input:
            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))

        # x0 = x[:, :, 0::2, 0::2, :]  # B D H/2 W/2 C
        # x1 = x[:, :, 1::2, 0::2, :]  # B D H/2 W/2 C
        # x2 = x[:, :, 0::2, 1::2, :]  # B D H/2 W/2 C
        # x3 = x[:, :, 1::2, 1::2, :]  # B D H/2 W/2 C

        x0 = x[:, 0::2, 0::2, 0::2, :]
        x1 = x[:, 1::2, 0::2, 0::2, :]
        x2 = x[:, 0::2, 1::2, 0::2, :]
        x3 = x[:, 0::2, 0::2, 1::2, :]
        x4 = x[:, 1::2, 0::2, 1::2, :]
        x5 = x[:, 0::2, 1::2, 0::2, :]
        x6 = x[:, 0::2, 0::2, 1::2, :]
        x7 = x[:, 1::2, 1::2, 1::2, :]

        # pdb.set_trace()

        x = torch.cat([x0, x1, x2, x3, x4, x5, x6, x7], -1)  # B D H/2 W/2 4*C

        x = self.norm(x)
        x = self.reduction(x)

        return x


# cache each stage results
@lru_cache()
def compute_mask(D, H, W, window_size, shift_size, device):
    img_mask = torch.zeros((1, D, H, W, 1), device=device)  # 1 Dp Hp Wp 1
    cnt = 0
    for d in slice(-window_size[0]), slice(-window_size[0], -shift_size[0]), slice(-shift_size[0], None):
        for h in slice(-window_size[1]), slice(-window_size[1], -shift_size[1]), slice(-shift_size[1], None):
            for w in slice(-window_size[2]), slice(-window_size[2], -shift_size[2]), slice(-shift_size[2], None):
                img_mask[:, d, h, w, :] = cnt
                cnt += 1
    # pdb.set_trace()
    mask_windows = window_partition(img_mask, window_size)  # nW, ws[0]*ws[1]*ws[2], 1
    mask_windows = mask_windows.squeeze(-1)  # nW, ws[0]*ws[1]*ws[2]
    attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
    attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
    return attn_mask


class BasicLayer(nn.Module):
    """A basic Swin Transformer layer for one stage.

    Args:
        dim (int): Number of feature channels
        depth (int): Depths of this stage.
        num_heads (int): Number of attention head.
        window_size (tuple[int]): Local window size. Default: (1,7,7).
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
    """

    def __init__(
        self,
        dim,
        depth,
        num_heads,
        window_size=(7, 7, 7),
        mlp_ratio=4.0,
        qkv_bias=False,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        norm_layer=nn.LayerNorm,
        downsample=None,
        use_checkpoint=False,
    ):
        super().__init__()
        self.window_size = window_size
        self.shift_size = tuple(i // 2 for i in window_size)
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        self.blocks = nn.ModuleList(
            [
                SwinTransformerBlock3D(
                    dim=dim,
                    num_heads=num_heads,
                    window_size=window_size,
                    shift_size=(0, 0, 0) if (i % 2 == 0) else self.shift_size,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    drop=drop,
                    attn_drop=attn_drop,
                    drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                    norm_layer=norm_layer,
                    use_checkpoint=use_checkpoint,
                )
                for i in range(depth)
            ]
        )

        self.downsample = downsample
        if self.downsample is not None:
            self.downsample = downsample(dim=dim, norm_layer=norm_layer)

    def forward(self, x):
        """Forward function.

        Args:
            x: Input feature, tensor size (B, C, D, H, W).
        """
        # calculate attention mask for SW-MSA

        B, C, D, H, W = x.shape
        window_size, shift_size = get_window_size((D, H, W), self.window_size, self.shift_size)
        x = rearrange(x, "b c d h w -> b d h w c")
        Dp = int(np.ceil(D / window_size[0])) * window_size[0]
        Hp = int(np.ceil(H / window_size[1])) * window_size[1]
        Wp = int(np.ceil(W / window_size[2])) * window_size[2]
        attn_mask = compute_mask(Dp, Hp, Wp, window_size, shift_size, x.device)
        for blk in self.blocks:
            # pdb.set_trace()
            x = blk(x, attn_mask)
        x = x.view(B, D, H, W, -1)

        if self.downsample is not None:
            x = self.downsample(x)
        x = rearrange(x, "b d h w c -> b c d h w")
        return x


class PatchEmbed3D(nn.Module):
    """Video to Patch Embedding.

    Args:
        patch_size (int): Patch token size. Default: (2,4,4).
        in_chans (int): Number of input video channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """

    def __init__(self, patch_size=(4, 4, 4), in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        self.patch_size = patch_size

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.proj = UnetBasicBlock(
            spatial_dims=3,
            in_channels=in_chans,
            out_channels=embed_dim,
            kernel_size=3,
            stride=2,
            norm_name=("INSTANCE", {"affine": True}),
        )
        # self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)

    def forward(self, x):
        """Forward function."""
        x = self.proj(x)  # B C D Wh Ww
        return x


# class PatchEmbed3D(nn.Module):
#     """ Video to Patch Embedding.
#
#     Args:
#         patch_size (int): Patch token size. Default: (2,4,4).
#         in_chans (int): Number of input video channels. Default: 3.
#         embed_dim (int): Number of linear projection output channels. Default: 96.
#         norm_layer (nn.Module, optional): Normalization layer. Default: None
#     """
#
#     def __init__(self, patch_size=(4, 4, 4), in_chans=3, embed_dim=96, norm_layer=None):
#         super().__init__()
#         self.patch_size = patch_size
#         self.in_chans = in_chans
#         self.embed_dim = embed_dim
#         self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
#
#         if norm_layer is not None:
#             self.norm = norm_layer(embed_dim)
#         else:
#             self.norm = None
#
#     def forward(self, x):
#         """Forward function."""
#         # padding
#         _, _, D, H, W = x.size()
#         if W % self.patch_size[2] != 0:
#             x = F.pad(x, (0, self.patch_size[2] - W % self.patch_size[2]))
#         if H % self.patch_size[1] != 0:
#             x = F.pad(x, (0, 0, 0, self.patch_size[1] - H % self.patch_size[1]))
#         if D % self.patch_size[0] != 0:
#             x = F.pad(x, (0, 0, 0, 0, 0, self.patch_size[0] - D % self.patch_size[0]))
#
#         x = self.proj(x)  # B C D Wh Ww
#         if self.norm is not None:
#             D, Wh, Ww = x.size(2), x.size(3), x.size(4)
#             x = x.flatten(2).transpose(1, 2)
#             x = self.norm(x)
#             x = x.transpose(1, 2).view(-1, self.embed_dim, D, Wh, Ww)
#         return x


class SwinTransformer3D(nn.Module):
    """Swin Transformer backbone.
        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
          https://arxiv.org/pdf/2103.14030

    Args:
        patch_size (int | tuple(int)): Patch size. Default: (4,4,4).
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        depths (tuple[int]): Depths of each Swin Transformer stage.
        num_heads (tuple[int]): Number of attention head of each stage.
        window_size (int): Window size. Default: 7.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: Truee
        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
        drop_rate (float): Dropout rate.
        attn_drop_rate (float): Attention dropout rate. Default: 0.
        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
        norm_layer: Normalization layer. Default: nn.LayerNorm.
        patch_norm (bool): If True, add normalization after patch embedding. Default: False.
        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
            -1 means not freezing any parameters.
    """

    def __init__(
        self,
        num_classes=0,
        img_size=(96, 96, 96),
        patch_size=(1, 1, 1),
        in_chans=4,
        embed_dim=96,
        depths=[2, 2, 6, 2],
        num_heads=[3, 6, 12, 24],
        window_size=(7, 7, 7),
        mlp_ratio=4.0,
        qkv_bias=True,
        qk_scale=None,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.2,
        norm_layer=nn.LayerNorm,
        patch_norm=False,
        use_checkpoint=False,
    ):
        super().__init__()

        self.in_chans = in_chans
        self.img_size = img_size
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.patch_norm = patch_norm
        self.window_size = window_size
        self.patch_size = patch_size

        # split image into non-overlapping patches
        self.patch_embed = PatchEmbed3D(
            patch_size=patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None,
        )

        self.pos_drop = nn.Dropout(p=drop_rate)

        # stochastic depth
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule

        self.layers1 = nn.ModuleList()
        self.layers2 = nn.ModuleList()
        self.layers3 = nn.ModuleList()
        self.layers4 = nn.ModuleList()
        for i_layer in range(self.num_layers):
            # pdb.set_trace()
            layer = BasicLayer(
                dim=int(embed_dim * 2**i_layer),
                depth=depths[i_layer],
                num_heads=num_heads[i_layer],
                window_size=window_size,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop=drop_rate,
                attn_drop=attn_drop_rate,
                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
                norm_layer=norm_layer,
                downsample=PatchMerging,
                # downsample=PatchMerging if i_layer < self.num_layers - 1 else None,
                use_checkpoint=use_checkpoint,
            )
            if i_layer == 0:
                self.layers1.append(layer)
            elif i_layer == 1:
                self.layers2.append(layer)
            elif i_layer == 2:
                self.layers3.append(layer)
            elif i_layer == 3:
                self.layers4.append(layer)

        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
        # add a norm layer for each output
        self.norm = norm_layer(2 * self.num_features)
        self.avgpool = nn.AdaptiveAvgPool1d(1)
        self.head = nn.Linear(2 * self.num_features, num_classes) if num_classes > 0 else nn.Identity()
        # self.apply(self._init_weights)

    def proj_out(self, x):
        # pdb.set_trace()
        n, ch, d, h, w = x.size()
        x = rearrange(x, "n c d h w -> n d h w c")
        norm_layer = nn.LayerNorm(ch).cuda()
        #         norm_layer = apex.normalization.FusedLayerNorm(ch).cuda()
        x = norm_layer(x.float())
        x = rearrange(x, "n d h w c -> n c d h w")
        return x

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=0.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {"absolute_pos_embed"}

    @torch.jit.ignore
    def no_weight_decay_keywords(self):
        return {"relative_position_bias_table"}

    def forward(self, x):
        _, _, D, H, W = x.size()
        x0 = self.patch_embed(x)
        x0 = self.pos_drop(x0)
        x1 = self.layers1[0](x0.contiguous())
        x2 = self.layers2[0](x1.contiguous())
        x3 = self.layers3[0](x2.contiguous())
        x4 = self.layers4[0](x3.contiguous())

        x4 = x4.reshape(-1, (D // 32) * (H // 32) * (W // 32), 2 * self.num_features)
        x_cls = self.norm(x4)  # B L C
        x_cls = self.avgpool(x_cls.transpose(1, 2))  # B C 1
        x_cls = torch.flatten(x_cls, 1)
        x_cls = self.head(x_cls)
        return x_cls

    def flops(self):
        flops = 0
        flops += self.patch_embed.flops()
        for i, layer in enumerate(self.layers):
            flops += layer.flops()
        flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2**self.num_layers)
        flops += self.num_features * self.num_classes
        return flops
